O'Reilly on Data

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. We won’t go into the mathematics or engineering of modern machine learning here.

Management 332

Management Machine Learning Experimentation Metrics 332

CIO Business Intelligence

NOVEMBER 7, 2022

CIOs seeking big wins in high business-impacting areas where there’s significant room to improve performance should review their data science, machine learning (ML), and AI projects. CIOs and CDOs should lead ModelOps and oversee the lifecycle Leaders can review and address issues if the data science teams struggle to develop models.

Machine Learning 128

Machine Learning Data Science Experimentation IoT 128

Dataiku

MAY 23, 2023

Once a data science project has progressed through the stages of data cleaning and preparation, analysis and experimentation, modeling, testing, and evaluation, it reaches a critical point.

Data Science 52

Data Science Experimentation Machine Learning Testing 52

CIO Business Intelligence

OCTOBER 5, 2023

Sandeep Davé knows the value of experimentation as well as anyone. As chief digital and technology officer at CBRE, Davé recognized early that the commercial real estate industry was ripe for AI and machine learning enhancements, and he and his team have tested countless use cases across the enterprise ever since.

Experimentation 119

Experimentation Strategy Machine Learning Interactive 119

CIO Business Intelligence

JUNE 14, 2023

The certification focuses on the seven domains of the analytics process: business problem framing, analytics problem framing, data, methodology selection, model building, deployment, and lifecycle management. Organization: AWS Price: US$300 How to prepare: Amazon offers free exam guides, sample questions, practice tests, and digital training.

Big Data 125

Big Data Data Analytics Analytics Predictive Modeling 125

CIO Business Intelligence

DECEMBER 19, 2023

This year’s technology darling and other machine learning investments have already impacted digital transformation strategies in 2023 , and boards will expect CIOs to update their AI transformation strategies frequently. Luckily, many are expanding budgets to do so. “94%

Digital Transformation 139

Digital Transformation Unstructured Data Machine Learning Risk Management 139

Domino Data Lab

NOVEMBER 14, 2019

James Murdoch, Chandan Singh, Karl Kumber, and Reza Abbasi-Asi’s recent paper, “Definitions, methods, and applications in interpretable machine learning” Introduction. Model interpretability continues to spark public discourse among industry. Yet there are tradeoffs to consider when selecting a model.

Modeling 71

Modeling Machine Learning Data Science Experimentation 71

Decision Management Solutions

DECEMBER 17, 2021

In the recent McKinsey article discussing designing next-generation credit-decisioning models they outlined four best practices for automated credit-decisioning models for banks as they continue their digital transformations. Digital lending based on high-performance credit-decisioning models, says McKinsey, lead to: Increased revenue.

Modeling 52

Modeling Experimentation Visualization Risk 52

Cloudera

JANUARY 30, 2019

Data scientists require on-demand access to data, powerful processing infrastructure, and multiple tools and libraries for development and experimentation. Run experiments with historical reference for hyperparameter tuning, feature engineering, grid searches, A/B testing and more. Sound familiar?

Data Science 72

Data Science Machine Learning Experimentation Cost-Benefit 72

Cloudera

OCTOBER 18, 2023

Historically, firms have relied on high-cost, third-party solutions to help identify savings opportunities, however, the landscape is rapidly changing, and the emergence of AI and machine learning (ML) has ushered in a new era of possibilities. Debugging: Identifying and fixing bugs in code is essential for application security.

Cost-Benefit 76

Cost-Benefit Testing Machine Learning Optimization 76

Domino Data Lab

AUGUST 6, 2021

Model Risk Management is about reducing bad consequences of decisions caused by trusting incorrect or misused model outputs. Systematically enabling model development and production deployment at scale entails use of an Enterprise MLOps platform, which addresses the full lifecycle including Model Risk Management.

Risk Management 64

Risk Management Risk Modeling Enterprise 64

Corinium

JUNE 6, 2019

Fractal’s recommendation is to take an incremental, test and learn approach to analytics to fully demonstrate the program value before making larger capital investments. There is usually a steep learning curve in terms of “doing AI right”, which is invaluable. What is the most common mistake people make around data?

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

CIO Business Intelligence

JANUARY 9, 2024

That quote aptly describes what Dell Technologies and Intel are doing to help our enterprise customers quickly, effectively, and securely deploy generative AI and large language models (LLMs).Many We’re using our own databases, testing against our own needs, and building around specific problem sets.

Enterprise 105

Enterprise Experimentation Modeling Data-driven 105

CIO Business Intelligence

FEBRUARY 21, 2024

One is knowledge of the emerging mega trends in technology — data, AI, and machine learning — and the other is understanding organizational culture needed to advance the technology goals and to inspire contributors,” he says. We developed a model to predict student outcomes based on metrics from historical evidence,” he says. “We

Recreation/Entertainment 124

Recreation/Entertainment Experimentation Modeling Data-driven 124

CIO Business Intelligence

SEPTEMBER 6, 2023

Since ChatGPT’s release in November of 2022, there have been countless conversations on the impact of similar large language models. The use of AI-generated code is still in an experimental phase for many organizations due to numerous uncertainties such as its impact on security, data privacy, copyright, and more.

Software 116

Software Experimentation Risk Data Lake 116

AWS Big Data

AUGUST 21, 2023

For the demo, we’re using the Amazon Titan foundation model hosted on Amazon Bedrock for embeddings, with no fine tuning. If you are interested in exploring the multi-modal model, please reach out to your AWS specialist. With OpenSearch’s Search Comparison Tool , you can compare the different approaches.

Data Processing 90

Data Processing Experimentation Visualization Metrics 90

DataRobot

SEPTEMBER 22, 2021

You have to pick the right model to meet expected performance goals. Train it, test it, tune it. You have to know how to take your model into production, manage it over time, enable the business to consume predictions from it, and use it to make the right decisions. You have to make your AI explainable and trustworthy.

Experimentation 85

Experimentation Testing Modeling Machine Learning 85

AWS Big Data

JANUARY 9, 2024

2023 was a year of rapid innovation within the artificial intelligence (AI) and machine learning (ML) space, and search has been a significant beneficiary of that progress. Traditional lexical search, based on term frequency models like BM25, is widely used and effective for many search applications.

Cost-Benefit 91

Cost-Benefit Visualization Modeling Machine Learning 91

The Unofficial Google Data Science Blog

APRIL 23, 2024

Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. That is true generally, not just in these experiments — spreading measurements out is generally better, if the straight-line model is a priori correct.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

IBM Big Data Hub

APRIL 18, 2024

While these large language model (LLM) technologies might seem like it sometimes, it’s important to understand that they are not the thinking machines promised by science fiction. Most experts categorize it as a powerful, but narrow AI model. A key trend is the adoption of multiple models in production.

Cost-Benefit 95

Cost-Benefit Modeling Manufacturing Interactive 95

Domino Data Lab

APRIL 7, 2019

A good NLP library will, for example, correctly transform free text sentences into structured features (like cost per hour and is diabetic ), that easily feed into a machine learning (ML) or deep learning (DL) pipeline (like predict monthly cost and classify high risk patients ). Training domain-specific models.

Deep Learning 110

Deep Learning Machine Learning Experimentation Modeling 110

DataRobot

JUNE 10, 2021

Traditionally, experimentation and observation was the only way to understand the physical-chemical properties of the molecule. If machine learning could contribute, this would allow for the faster invention of new compounds tailored for particular aromatic signatures.

Metrics 52

Metrics Machine Learning Visualization Experimentation 52

DataRobot

APRIL 19, 2021

Most modern artificial intelligence systems are powered by machine learning algorithms , which learn by example. Data scientists train the algorithms using datasets that contain curated learning examples. The model was worthless. After the initial shock and disappointment, I reviewed my work.

Machine Learning 52

Machine Learning Visualization Data-driven Data Science 52

Domino Data Lab

AUGUST 6, 2021

When you factor in the requirements of a business-critical machine learning model in a working enterprise environment, the old cat-herding meme won’t even get a smile. Develop: includes accessing and preparing data and algorithms, researching and development of models and experimentation. 4 Steps in the DSLC.

Data Science 75

Data Science Testing Modeling Publishing 75

Data Science and Beyond

SEPTEMBER 19, 2016

Some people equate predictive modelling with data science, thinking that mastering various machine learning techniques is the key that unlocks the mysteries of the field. However, there is much more to data science than the What and How of predictive modelling. Causality and experimentation.

Data Science 66

Data Science Predictive Modeling Experimentation Deep Learning 66

Domino Data Lab

JUNE 13, 2019

Recently, Chhavi Yadav (NYU) and Leon Bottou (Facebook AI Research and NYU) indicated in their paper, “ Cold Case: The Lost MNIST Digits ”, how they reconstructed the MNIST (Modified National Institute of Standards and Technology) dataset and added 50,000 samples to the test set for a total of 60,000 samples. Did they overfit the test set?

Testing 83

Testing Data Science Experimentation Metadata 83

AWS Big Data

MAY 2, 2023

Test the feature To test this feature, run the producer DAG. Removal of experimental Smart Sensors. This feature is particularly useful if you want to externally process various files, evaluate multiple machine learning models, or extraneously process a varied amount of data based on a SQL request.

Testing 100

Testing Experimentation Management Metadata 100

Occam's Razor

MARCH 23, 2021

A key part of how this manifested in our work was doing truly super-advanced machine-learning powered analysis to answer hard questions that few can successfully. The first component is a gloriously scaled global creative pre-testing program. Matched market tests. This is of course exciting and very cool. The slow music.

Analytics 131

Analytics Scorecard Strategy Testing 131

Insight

MARCH 12, 2020

The AIgent was built with BERT, Google’s state-of-the-art language model. In this article, I will discuss the construction of the AIgent, from data collection to model assembly. Features: DistilBERT Text Embeddings Once I had a raw dataset, I could begin engineering features and building a natural language processing (NLP) model.

Modeling 59

Modeling Metadata Publishing Sales 59

DataRobot Blog

JANUARY 10, 2023

Most, if not all, machine learning (ML) models in production today were born in notebooks before they were put into production. Data science teams of all sizes need a productive, collaborative method for rapid AI experimentation. Capabilities Beyond Classic Jupyter for End-to-end Experimentation.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

Domino Data Lab

AUGUST 5, 2019

As more companies become model-driven , data scientists are uniquely positioned to drive innovation and to help their companies remain economically strong. the model context ) when building and using models enable data scientists to directly impact business problems. data munging, building models, etc.).

Data Science 93

Data Science Data-driven Modeling Machine Learning 93

Domino Data Lab

OCTOBER 2, 2019

This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by Andrew Kelleher and Adam Kelleher. The case that might be familiar to you is an AB test. A complementary Domino project is available. .

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

Ontotext

APRIL 3, 2024

W hen you try to automate business processes, semantics, and machine learning , knowledge graphs can bring a lot of value. Nimit Mehta: I think that 2024 is going to be a buckle-down year, but, at the same time, we’ll see a rapid explosion of experimentation. Aurelije Zovko : For me, it is a digital transformation. Maybe later.

Technology 59

Technology Risk Digital Transformation Measurement 59

O'Reilly on Data

MAY 14, 2020

This role includes everything a traditional PM does, but also requires an operational understanding of machine learning software development, along with a realistic view of its capabilities and limitations. Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model.

Management 363

Management Experimentation B2B Machine Learning 363

O'Reilly on Data

OCTOBER 19, 2021

Much has been written about struggles of deploying machine learning projects to production. This approach has worked well for software development, so it is reasonable to assume that it could address struggles related to deploying machine learning in production too. However, the concept is quite abstract.

IT 346

IT Testing Experimentation Software 346

CIO Business Intelligence

MAY 1, 2024

Gen AI takes us from single-use models of machine learning (ML) to AI tools that promise to be a platform with uses in many areas, but you still need to validate they’re appropriate for the problems you want solved, and that your users know how to use gen AI effectively.

Cost-Benefit 138

Cost-Benefit Metrics Insurance Measurement 138

Domino Data Lab

DECEMBER 4, 2020

NCA doesn’t require the assumption of a specific compartmental model for either drug or metabolite; it is instead assumption-free and therefore easily automated [1]. PharmaceUtical Modeling And Simulation (or PUMAS) is a suite of tools to perform quantitative analytics for pharmaceutical drug development [2]. Mean residence time.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59